Analyst: Catarina Franco and Bini Ramachandran
User: Alex Fulton
PI: Anne Willis
Title of the project: RBPome of Huh-7 and HepG2 cell lines
This report summarizes sample handling for mass spectrometry and data analysis (Proteome Discoverer analysis and any additional data manipulation necessary).
The RBPome of both cell lines was extracted using OOPS protocol and samples from RNase treated and non-treated samples were analysed by mass spectrometry (interfase, organic phase and total input). Bellow there is a table with the correspondence of the internal samples number and sample designation.
samples <- read_excel("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/Report/AlexFulton_OOPS_1854746152/samplesDesignation.xlsx")
datatable(samples)
DownloadSamples <- samples %>%
download_this(
output_name = "sampleDesignation dataset",
output_extension = ".csv",
button_label = "Download sample designation as csv",
button_type = "default",
has_icon = TRUE,
icon = "fa fa-save"
)
DownloadSamples
OBS: 1:5 dilution of samples were not in the range. The whole of the samples were taken for digestion
protein1 <- read_excel("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/Report/AlexFulton_OOPS_1854746152/OrganicPhase_totalProtein_estimation.xlsx")
datatable(protein1)
Downloadprotein1 <- protein1 %>%
download_this(
output_name = "Organic phase proteinEstimation dataset",
output_extension = ".xlsx",
button_label = "Download organic phase protein estimation as xlsx",
button_type = "default",
has_icon = TRUE,
icon = "fa fa-save"
)
Downloadprotein1
OBS: HepG2_Rnase- samples appeared cloudy with a precipitate in some of the samples. Frozen the samples to check the completeness of digestion before acidification and hydrolysis of RapiGest.
Spin down 4 samples, one from each group (4, 14, 24, 34) were diluted 1:100 and injected 2 uL to check the completeness of digestion
Note: Injected 2 uL of all the samples over a 30 min method to range based on the intensity of identified peptides. However, the peptide identification varied across the samples based on the cell line and Rnase treatment. Normalization set to injection volume (12 uL of 1:20 dilution ran over 90 min gradient).
OBS: 1:5 dilution of samples were not in the range. The whole of the samples were taken for digestion.
Note: Injected 2 uL of 1:5 diluted samples 9, 19, 29, & 39 to range over a 30 min method. Based on this, 6 uL of all samples at 1:5 dilution were ran over a 90 min gradient.
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
## 1
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_1.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_2.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_3.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_4.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_5.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_11.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_12.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_13.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_14.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_15.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_21.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_22.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_23.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_24.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_24.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_31.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_32_20220129174611.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_33.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_34.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_35.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_6.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_7.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_8.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_9.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_10.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_16.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_17.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_18.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_19.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_20.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_26.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_27.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_29.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_30.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_36.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_37.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_38.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_39.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
## overlapping different raw files from the same LFQ experiment
#Extract the mzML from the raw files using MS convert
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_40.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample<- subset(hd, select =c("retentionTime", "basePeakIntensity"))
ggplot(data=sample, aes(x=retentionTime, y=basePeakIntensity)) +
geom_line(aes(color="#FF9999", alpha=1), show.legend = FALSE) + theme_classic()
Soon
In these plots figures you can see how is the distribution of the total ion current (total intensity of all the ions going inside of the mass spec) per sample.
## 1
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_1.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample1<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample1["Sample"] <- "Sample1"
## 2
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_2.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample2<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample2["Sample"] <- "Sample2"
## 3
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_3.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample3<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample3["Sample"] <- "Sample3"
## 4
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_4.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample4<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample4["Sample"] <- "Sample4"
## 3
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_3.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample3<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample3["Sample"] <- "Sample3"
## 5
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_5.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample5<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample5["Sample"] <- "Sample5"
## 11
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_11.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample11<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample11["Sample"] <- "Sample11"
## 12
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_12.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample12<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample12["Sample"] <- "Sample12"
## 13
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_13.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample13<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample13["Sample"] <- "Sample13"
## 14
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_14.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample14<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample14["Sample"] <- "Sample14"
## 15
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_15.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample15<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample15["Sample"] <- "Sample15"
## 21
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_21.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample21<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample21["Sample"] <- "Sample21"
## 22
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_22.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample22<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample22["Sample"] <- "Sample22"
## 23
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_23.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample23<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample23["Sample"] <- "Sample23"
## 24
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_24.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample24<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample24["Sample"] <- "Sample24"
## 25
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_25.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample25<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample25["Sample"] <- "Sample25"
## 31
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_31.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample31<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample31["Sample"] <- "Sample31"
## 32
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_32_20220129174611.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample32<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample32["Sample"] <- "Sample32"
## 33
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_33.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample33<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample33["Sample"] <- "Sample33"
## 34
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_34.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample34<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample34["Sample"] <- "Sample34"
## 35
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_LFQ_35.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample35<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample35["Sample"] <- "Sample35"
MergedSamples<- rbind(sample1, sample2, sample3, sample4, sample5, sample11, sample12, sample13, sample14, sample15, sample21, sample22, sample23, sample24, sample25, sample31, sample32, sample33, sample34,
sample35)
nb.cols <- 30
mycolors <- colorRampPalette(brewer.pal(8, "Set2"))(nb.cols)
ggplot(MergedSamples, aes(Sample, log10(totIonCurrent))) + geom_boxplot(aes(color=Sample), show.legend = FALSE) +
coord_trans(y ='log10') + theme_classic() +
scale_color_manual(values = mycolors) +
theme(axis.text.x = element_text(angle = 45, hjust=1)) +
scale_x_discrete (limits = c("Sample1", "Sample2", "Sample3", "Sample4", "Sample5", "Sample11", "Sample12", "Sample13", "Sample14", "Sample15", "Sample21", "Sample22", "Sample23", "Sample24", "Sample25", "Sample31", "Sample32", "Sample33", "Sample34", "Sample35"))
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_6.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample6<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample6["Sample"] <- "Sample6"
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_7.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample7<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample7["Sample"] <- "Sample7"
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_8.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample8<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample8["Sample"] <- "Sample8"
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_9.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample9<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample9["Sample"] <- "Sample9"
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_10.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample10<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample10["Sample"] <- "Sample10"
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_16.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample16<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample16["Sample"] <- "Sample16"
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_17.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample17<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample17["Sample"] <- "Sample17"
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_18.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample18<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample18["Sample"] <- "Sample18"
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_19.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample19<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample19["Sample"] <- "Sample19"
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_20.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample20<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample20["Sample"] <- "Sample20"
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_26.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample26<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample26["Sample"] <- "Sample26"
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_27.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample27<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample27["Sample"] <- "Sample27"
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_29.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample29<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample29["Sample"] <- "Sample29"
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_30.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample30<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample30["Sample"] <- "Sample30"
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_36.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample36<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample36["Sample"] <- "Sample36"
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_37.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample37<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample37["Sample"] <- "Sample37"
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_38.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample38<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample38["Sample"] <- "Sample38"
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_39.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample39<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample39["Sample"] <- "Sample39"
mzf <- ("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/mzML/1854746152_AF_OOPS_Organic_LFQ_40.mzML")
ms <- openMSfile(mzf)
hd <- header(ms)
sample40<- subset(hd, select =c("retentionTime", "totIonCurrent"))
sample40["Sample"] <- "Sample40"
MergedSamples<- rbind(sample6, sample7, sample8, sample9, sample10, sample16, sample17, sample18, sample19, sample20, sample26, sample27, sample29, sample30, sample36, sample37, sample38, sample39,
sample40)
nb.cols <- 30
mycolors <- colorRampPalette(brewer.pal(8, "Set2"))(nb.cols)
ggplot(MergedSamples, aes(Sample, log10(totIonCurrent))) + geom_boxplot(aes(color=Sample), show.legend = FALSE) +
coord_trans(y ='log10') + theme_classic() +
scale_color_manual(values = mycolors) +
theme(axis.text.x = element_text(angle = 45, hjust=1)) +
scale_x_discrete (limits = c("Sample6", "Sample7", "Sample8", "Sample9", "Sample10", "Sample16", "Sample17", "Sample18", "Sample19", "Sample20", "Sample26", "Sample27", "Sample29", "Sample30", "Sample36", "Sample37", "Sample38", "Sample39", "Sample40"))
Soon
| Sample group | Proteins | Peptide groups | Unique Proteins | Unique peptides groups |
|---|---|---|---|---|
| HepG2 interfase - | 2232 | 10659 | 393 | 3964 |
| HepG2 interphase + | 2655 | 12432 | 816 | 5737 |
| Huh7 interfase - | 476 | 1103 | 7 | 158 |
| Huh7 interfase + | 2490 | 13238 | 2021 | 12293 |
Below you can find a tables of the unique proteins found only in the RNAase non-treated samples
HepG2 unique proteins on the RNAase non-treated samples
HepG2_unique_proteins <- read_excel("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/PD exports/Interfaphase/1854746152_AF_HepG2_interfase_uniqueNON_TREATED.xlsx")
datatable(HepG2_unique_proteins)
DownloadHepG2_unique_proteins <- HepG2_unique_proteins %>%
download_this(
output_name = "HepG2_unique_proteins dataset",
output_extension = ".xlsx",
button_label = "Download HepG2_unique_proteins dataset as xlsx",
button_type = "default",
has_icon = TRUE,
icon = "fa fa-save"
)
DownloadHepG2_unique_proteins
Huh7 unique proteins on the RNAase non-treated samples
Huh7_unique_proteins <- read_excel("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/PD exports/Interfaphase/1854746152_AF_Huh7_interfase_uniqueNON_TREATED.xlsx")
datatable(Huh7_unique_proteins)
DownloadHuh7_unique_proteins <- Huh7_unique_proteins %>%
download_this(
output_name = "Huh7_unique_proteins dataset",
output_extension = ".xlsx",
button_label = "Download Huh7_unique_proteins dataset as xlsx",
button_type = "default",
has_icon = TRUE,
icon = "fa fa-save"
)
DownloadHuh7_unique_proteins
| Sample group | Proteins | Peptide groups |
|---|---|---|
| HepG2 organic phase | 2078 | 2374 |
| Huh7 organic phase | 1917 | 10350 |
HepG2 dataset
# Read proteins data in
f <- "/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/PD exports/Interfaphase/1854746152_AF_HepG2_interfase_NORM-(1)_Proteins.txt" #make sure r-friendly headers
e <- grepEcols(f, "Abundance F", split = "\t") # Locate the Raw Abundance Values from the PD file that is separated by tabs
x <- readMSnSet2(f, e, sep = "\t") # Get the Raw Abundance Values from the PD file
## Data annotation
# create pData
Sample <- gsub("Abundance\\.F1\\.\\w*\\.Sample\\.(\\d\\.+\\w*)", "\\1", colnames(exprs(x)))
tmp <- data.frame(do.call(rbind, strsplit(Sample, "[..]")))
tmp<-tmp[,c(3,1)]
names(tmp) <- c("Sample", "Replicate")
## Number of high confidance proteins
cp <- data.frame(table(fData(x)$Protein.FDR.Confidence))
colnames(cp)[1] <- "Protein.FDR.Confidence"
tmp <- x[fData(x)$Protein.FDR.Confidence == "High",]
## Number of missing values
naplot(tmp)
Huh7 dataset
# Read proteins data in
f <- "/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/PD exports/Interfaphase/1854746152_AF_Huh7_NORM_Proteins.txt" #make sure r-friendly headers
e <- grepEcols(f, "Abundance F", split = "\t") # Locate the Raw Abundance Values from the PD file that is separated by tabs
x <- readMSnSet2(f, e, sep = "\t") # Get the Raw Abundance Values from the PD file
## Data annotation
# create pData
Sample <- gsub("Abundance\\.F1\\.\\w*\\.Sample\\.(\\d\\.+\\w*)", "\\1", colnames(exprs(x)))
tmp <- data.frame(do.call(rbind, strsplit(Sample, "[..]")))
tmp<-tmp[,c(3,1)]
names(tmp) <- c("Sample", "Replicate")
## Number of high confidance proteins
cp <- data.frame(table(fData(x)$Protein.FDR.Confidence))
colnames(cp)[1] <- "Protein.FDR.Confidence"
tmp <- x[fData(x)$Protein.FDR.Confidence == "High",]
## Number of missing values
naplot(tmp)
HepG2 dataset
# Read proteins data in
f <- "/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/PD exports/Interfaphase/1854746152_AF_HepG2_organicPhase_NORM_percolator_Proteins.txt" #make sure r-friendly headers
e <- grepEcols(f, "Abundance F", split = "\t") # Locate the Raw Abundance Values from the PD file that is separated by tabs
x <- readMSnSet2(f, e, sep = "\t") # Get the Raw Abundance Values from the PD file
## Data annotation
# create pData
Sample <- gsub("Abundance\\.F1\\.\\w*\\.Sample\\.(\\d\\.+\\w*)", "\\1", colnames(exprs(x)))
tmp <- data.frame(do.call(rbind, strsplit(Sample, "[..]")))
tmp<-tmp[,c(3,1)]
names(tmp) <- c("Sample", "Replicate")
## Number of high confidance proteins
cp <- data.frame(table(fData(x)$Protein.FDR.Confidence))
colnames(cp)[1] <- "Protein.FDR.Confidence"
tmp <- x[fData(x)$Protein.FDR.Confidence == "High",]
## Number of missing values
naplot(tmp)
Huh7 dataset
# Read proteins data in
f <- "/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/PD exports/Interfaphase/1854746152_AF_Huh7_organicPhase_NORM_percolator_Proteins.txt" #make sure r-friendly headers
e <- grepEcols(f, "Abundance F", split = "\t") # Locate the Raw Abundance Values from the PD file that is separated by tabs
x <- readMSnSet2(f, e, sep = "\t") # Get the Raw Abundance Values from the PD file
## Data annotation
# create pData
Sample <- gsub("Abundance\\.F1\\.\\w*\\.Sample\\.(\\d\\.+\\w*)", "\\1", colnames(exprs(x)))
tmp <- data.frame(do.call(rbind, strsplit(Sample, "[..]")))
tmp<-tmp[,c(3,1)]
names(tmp) <- c("Sample", "Replicate")
## Number of high confidance proteins
cp <- data.frame(table(fData(x)$Protein.FDR.Confidence))
colnames(cp)[1] <- "Protein.FDR.Confidence"
tmp <- x[fData(x)$Protein.FDR.Confidence == "High",]
## Number of missing values
naplot(tmp)
Soon
Boxplot of raw protein abundances
# read the proteins.csv file
data_start <- read.csv("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/PD exports/Interfaphase/1854746152_AF_HepG2_interfase_NORM-(1)_Proteins.csv")
# master proteins only
newdata <- subset(data_start, Master=='IsMasterProtein')
# Protein FDR confidence = high
newdata2 <- subset(newdata, Protein.FDR.Confidence.Combined=='High')
# Select raw abundances
newdata3 <- newdata2[c(7, 65:74)]
# filter out proteins missing values
data_no_na <- na.omit(newdata3)
# remove sp Accessions (contaminants)
data_no_aa_noSP <- subset(data_no_na, Accession!='sp')
# fix the column headers
col_headers <- colnames(data_no_aa_noSP)
colnames(data_no_aa_noSP) <- col_headers
# save the annotations (gene symbol and protein accession) and remove from data frame
data_raw <- as.data.frame(data_no_aa_noSP[2:11])
Abundance.F1.Sample.non.treated <- data_raw %>%
select(Abundance.F1.Sample.non.treated)
Abundance.F1.Sample.non.treated["sample"] <- "Abundance.F1.Sample.non.treated"
names(Abundance.F1.Sample.non.treated)[names(Abundance.F1.Sample.non.treated) == "Abundance.F1.Sample.non.treated"] <- "RawAbundance"
Abundance.F2.Sample.non.treated <- data_raw %>%
select(Abundance.F2.Sample.non.treated)
Abundance.F2.Sample.non.treated["sample"] <- "Abundance.F2.Sample.non.treated"
names(Abundance.F2.Sample.non.treated)[names(Abundance.F2.Sample.non.treated) == "Abundance.F2.Sample.non.treated"] <- "RawAbundance"
Abundance.F3.Sample.non.treated <- data_raw %>%
select(Abundance.F3.Sample.non.treated)
Abundance.F3.Sample.non.treated["sample"] <- "Abundance.F3.Sample.non.treated"
names(Abundance.F3.Sample.non.treated)[names(Abundance.F3.Sample.non.treated) == "Abundance.F3.Sample.non.treated"] <- "RawAbundance"
Abundance.F4.Sample.non.treated <- data_raw %>%
select(Abundance.F4.Sample.non.treated)
Abundance.F4.Sample.non.treated["sample"] <- "Abundance.F4.Sample.non.treated"
names(Abundance.F4.Sample.non.treated)[names(Abundance.F4.Sample.non.treated) == "Abundance.F4.Sample.non.treated"] <- "RawAbundance"
Abundance.F5.Sample.non.treated <- data_raw %>%
select(Abundance.F5.Sample.non.treated)
Abundance.F5.Sample.non.treated["sample"] <- "Abundance.F5.Sample.non.treated"
names(Abundance.F5.Sample.non.treated)[names(Abundance.F5.Sample.non.treated) == "Abundance.F5.Sample.non.treated"] <- "RawAbundance"
Abundance.F6.Sample.treated <- data_raw %>%
select(Abundance.F6.Sample.treated)
Abundance.F6.Sample.treated["sample"] <- "Abundance.F6.Sample.treated"
names(Abundance.F6.Sample.treated)[names(Abundance.F6.Sample.treated) == "Abundance.F6.Sample.treated"] <- "RawAbundance"
Abundance.F7.Sample.treated <- data_raw %>%
select(Abundance.F7.Sample.treated)
Abundance.F7.Sample.treated["sample"] <- "Abundance.F7.Sample.treated"
names(Abundance.F7.Sample.treated)[names(Abundance.F7.Sample.treated) == "Abundance.F7.Sample.treated"] <- "RawAbundance"
Abundance.F8.Sample.treated <- data_raw %>%
select(Abundance.F8.Sample.treated)
Abundance.F8.Sample.treated["sample"] <- "Abundance.F8.Sample.treated"
names(Abundance.F8.Sample.treated)[names(Abundance.F8.Sample.treated) == "Abundance.F8.Sample.treated"] <- "RawAbundance"
Abundance.F9.Sample.treated <- data_raw %>%
select(Abundance.F9.Sample.treated)
Abundance.F9.Sample.treated["sample"] <- "Abundance.F9.Sample.treated"
names(Abundance.F9.Sample.treated)[names(Abundance.F9.Sample.treated) == "Abundance.F9.Sample.treated"] <- "RawAbundance"
Abundance.F10.Sample.treated <- data_raw %>%
select(Abundance.F10.Sample.treated)
Abundance.F10.Sample.treated["sample"] <- "Abundance.F10.Sample.treated"
names(Abundance.F10.Sample.treated)[names(Abundance.F10.Sample.treated) == "Abundance.F10.Sample.treated"] <- "RawAbundance"
##Add datasets vertically
MergedSamplesHepG2<- rbind(Abundance.F1.Sample.non.treated, Abundance.F2.Sample.non.treated, Abundance.F3.Sample.non.treated, Abundance.F4.Sample.non.treated, Abundance.F5.Sample.non.treated, Abundance.F6.Sample.treated, Abundance.F7.Sample.treated, Abundance.F8.Sample.treated, Abundance.F9.Sample.treated, Abundance.F10.Sample.treated)
SamplesRaw <- read.csv("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/Report/Ranalysis/MergedSamplesHepG2.csv")
# Define the number of colors you want
nb.cols <- 10
mycolors <- colorRampPalette(brewer.pal(8, "Set2"))(nb.cols)
# Create a ggplot with 18 colors
# Use scale_fill_manual
ggplot(SamplesRaw, aes(x=sample, y=log10(RawAbundance), color=sample)) +
geom_boxplot(show.legend = FALSE) +
labs(x="group") + theme_classic() +
theme(axis.text.x = element_text(angle = 45, hjust=1), axis.title.x = element_blank()) +
ggtitle("Raw abundances HepG2") +
scale_x_discrete (limits = c("Abundance.F1.Sample.non.treated", "Abundance.F2.Sample.non.treated", "Abundance.F3.Sample.non.treated", "Abundance.F4.Sample.non.treated", "Abundance.F5.Sample.non.treated", "Abundance.F6.Sample.treated", "Abundance.F7.Sample.treated", "Abundance.F8.Sample.treated", "Abundance.F9.Sample.treated", "Abundance.F10.Sample.treated")) +
scale_color_manual(values=c("Abundance.F1.Sample.non.treated" = "#FF9999", "Abundance.F2.Sample.non.treated"= "#FF9999", "Abundance.F3.Sample.non.treated"= "#FF9999", "Abundance.F4.Sample.non.treated"= "#FF9999", "Abundance.F5.Sample.non.treated"= "#FF9999", "Abundance.F6.Sample.treated"= "#99CCFF", "Abundance.F7.Sample.treated"= "#99CCFF", "Abundance.F8.Sample.treated"= "#99CCFF", "Abundance.F9.Sample.treated"= "#99CCFF", "Abundance.F10.Sample.treated"= "#99CCFF"))
# read the proteins.csv file
data_start <- read.csv("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/PD exports/Interfaphase/1854746152_AF_Huh7_NORM_Proteins.csv")
# master proteins only
newdata <- subset(data_start, Master=='IsMasterProtein')
# Protein FDR confidence = high
newdata2 <- subset(newdata, Protein.FDR.Confidence.Combined=='High')
# Select raw abundances
newdata3 <- newdata2[c(7, 62:71)]
# filter out proteins missing values
data_no_na <- na.omit(newdata3)
# remove sp Accessions (contaminants)
data_no_aa_noSP <- subset(data_no_na, Accession!='sp')
# fix the column headers
col_headers <- colnames(data_no_aa_noSP)
colnames(data_no_aa_noSP) <- col_headers
# save the annotations (gene symbol and protein accession) and remove from data frame
data_raw <- as.data.frame(data_no_aa_noSP[2:11])
Abundance.F11.Sample.non.treated <- data_raw %>%
select(Abundance.F11.Sample.non.treated)
Abundance.F11.Sample.non.treated["sample"] <- "Abundance.F11.Sample.non.treated"
names(Abundance.F11.Sample.non.treated)[names(Abundance.F11.Sample.non.treated) == "Abundance.F11.Sample.non.treated"] <- "RawAbundance"
Abundance.F12.Sample.non.treated <- data_raw %>%
select(Abundance.F12.Sample.non.treated)
Abundance.F12.Sample.non.treated["sample"] <- "Abundance.F12.Sample.non.treated"
names(Abundance.F12.Sample.non.treated)[names(Abundance.F12.Sample.non.treated) == "Abundance.F12.Sample.non.treated"] <- "RawAbundance"
Abundance.F13.Sample.non.treated <- data_raw %>%
select(Abundance.F13.Sample.non.treated)
Abundance.F13.Sample.non.treated["sample"] <- "Abundance.F13.Sample.non.treated"
names(Abundance.F13.Sample.non.treated)[names(Abundance.F13.Sample.non.treated) == "Abundance.F13.Sample.non.treated"] <- "RawAbundance"
Abundance.F14.Sample.non.treated <- data_raw %>%
select(Abundance.F14.Sample.non.treated)
Abundance.F14.Sample.non.treated["sample"] <- "Abundance.F14.Sample.non.treated"
names(Abundance.F14.Sample.non.treated)[names(Abundance.F14.Sample.non.treated) == "Abundance.F14.Sample.non.treated"] <- "RawAbundance"
Abundance.F15.Sample.non.treated <- data_raw %>%
select(Abundance.F15.Sample.non.treated)
Abundance.F15.Sample.non.treated["sample"] <- "Abundance.F15.Sample.non.treated"
names(Abundance.F15.Sample.non.treated)[names(Abundance.F15.Sample.non.treated) == "Abundance.F15.Sample.non.treated"] <- "RawAbundance"
Abundance.F16.Sample.treated <- data_raw %>%
select(Abundance.F16.Sample.treated)
Abundance.F16.Sample.treated["sample"] <- "Abundance.F16.Sample.treated"
names(Abundance.F16.Sample.treated)[names(Abundance.F16.Sample.treated) == "Abundance.F16.Sample.treated"] <- "RawAbundance"
Abundance.F17.Sample.treated <- data_raw %>%
select(Abundance.F17.Sample.treated)
Abundance.F17.Sample.treated["sample"] <- "Abundance.F17.Sample.treated"
names(Abundance.F17.Sample.treated)[names(Abundance.F17.Sample.treated) == "Abundance.F17.Sample.treated"] <- "RawAbundance"
Abundance.F18.Sample.treated <- data_raw %>%
select(Abundance.F18.Sample.treated)
Abundance.F18.Sample.treated["sample"] <- "Abundance.F18.Sample.treated"
names(Abundance.F18.Sample.treated)[names(Abundance.F18.Sample.treated) == "Abundance.F18.Sample.treated"] <- "RawAbundance"
Abundance.F19.Sample.treated <- data_raw %>%
select(Abundance.F19.Sample.treated)
Abundance.F19.Sample.treated["sample"] <- "Abundance.F19.Sample.treated"
names(Abundance.F19.Sample.treated)[names(Abundance.F19.Sample.treated) == "Abundance.F19.Sample.treated"] <- "RawAbundance"
Abundance.F20.Sample.treated <- data_raw %>%
select(Abundance.F20.Sample.treated)
Abundance.F20.Sample.treated["sample"] <- "Abundance.F20.Sample.treated"
names(Abundance.F20.Sample.treated)[names(Abundance.F20.Sample.treated) == "Abundance.F20.Sample.treated"] <- "RawAbundance"
##Add datasets vertically
MergedSamplesHuh7<- rbind(Abundance.F11.Sample.non.treated, Abundance.F12.Sample.non.treated, Abundance.F13.Sample.non.treated, Abundance.F14.Sample.non.treated, Abundance.F15.Sample.non.treated, Abundance.F16.Sample.treated, Abundance.F17.Sample.treated, Abundance.F18.Sample.treated, Abundance.F9.Sample.treated, Abundance.F20.Sample.treated)
SamplesRaw <- read.csv("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/Report/Ranalysis/MergedSamplesHuh7.csv")
ggplot(SamplesRaw, aes(x=sample, y=log10(RawAbundance), color=sample)) +
geom_boxplot(show.legend = FALSE) + coord_trans(y = "log10")+
labs(x="group") + theme_classic() +
theme(axis.text.x = element_text(angle = 45, hjust=1), axis.title.x = element_blank()) +
ggtitle("Raw abundances Huh7") +
scale_x_discrete (limits = c("Abundance.F11.Sample.non.treated", "Abundance.F12.Sample.non.treated", "Abundance.F13.Sample.non.treated", "Abundance.F14.Sample.non.treated", "Abundance.F15.Sample.non.treated", "Abundance.F16.Sample.treated", "Abundance.F17.Sample.treated", "Abundance.F18.Sample.treated", "Abundance.F9.Sample.treated", "Abundance.F20.Sample.treated")) +
scale_color_manual(values=c("Abundance.F11.Sample.non.treated" = "#FF9999", "Abundance.F12.Sample.non.treated"= "#FF9999", "Abundance.F13.Sample.non.treated"= "#FF9999", "Abundance.F14.Sample.non.treated"= "#FF9999", "Abundance.F15.Sample.non.treated"= "#FF9999", "Abundance.F16.Sample.treated"= "#99CCFF", "Abundance.F17.Sample.treated"= "#99CCFF", "Abundance.F18.Sample.treated"= "#99CCFF", "Abundance.F9.Sample.treated"= "#99CCFF", "Abundance.F20.Sample.treated"= "#99CCFF"))
# read the proteins.csv file
data_start <- read.csv("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/PD exports/Interfaphase/1854746152_AF_HepG2_organicPhase_NORM_percolator_Proteins.csv")
# master proteins only
newdata <- subset(data_start, Master=='IsMasterProtein')
# Protein FDR confidence = high
newdata2 <- subset(newdata, Protein.FDR.Confidence.Combined=='High')
# Select raw abundances
newdata3 <- newdata2[c(7, 65:74)]
# filter out proteins missing values
data_no_na <- na.omit(newdata3)
# remove sp Accessions (contaminants)
data_no_aa_noSP <- subset(data_no_na, Accession!='sp')
# fix the column headers
col_headers <- colnames(data_no_aa_noSP)
colnames(data_no_aa_noSP) <- col_headers
# save the annotations (gene symbol and protein accession) and remove from data frame
data_raw <- as.data.frame(data_no_aa_noSP[2:11])
Abundance.F21.Sample.non.treated <- data_raw %>%
select(Abundance.F21.Sample.non.treated)
Abundance.F21.Sample.non.treated["sample"] <- "Abundance.F21.Sample.non.treated"
names(Abundance.F21.Sample.non.treated)[names(Abundance.F21.Sample.non.treated) == "Abundance.F21.Sample.non.treated"] <- "RawAbundance"
Abundance.F22.Sample.non.treated <- data_raw %>%
select(Abundance.F22.Sample.non.treated)
Abundance.F22.Sample.non.treated["sample"] <- "Abundance.F22.Sample.non.treated"
names(Abundance.F22.Sample.non.treated)[names(Abundance.F22.Sample.non.treated) == "Abundance.F22.Sample.non.treated"] <- "RawAbundance"
Abundance.F23.Sample.non.treated <- data_raw %>%
select(Abundance.F23.Sample.non.treated)
Abundance.F23.Sample.non.treated["sample"] <- "Abundance.F23.Sample.non.treated"
names(Abundance.F23.Sample.non.treated)[names(Abundance.F23.Sample.non.treated) == "Abundance.F23.Sample.non.treated"] <- "RawAbundance"
Abundance.F24.Sample.non.treated <- data_raw %>%
select(Abundance.F24.Sample.non.treated)
Abundance.F24.Sample.non.treated["sample"] <- "Abundance.F24.Sample.non.treated"
names(Abundance.F24.Sample.non.treated)[names(Abundance.F24.Sample.non.treated) == "Abundance.F24.Sample.non.treated"] <- "RawAbundance"
Abundance.F25.Sample.non.treated <- data_raw %>%
select(Abundance.F25.Sample.non.treated)
Abundance.F25.Sample.non.treated["sample"] <- "Abundance.F25.Sample.non.treated"
names(Abundance.F25.Sample.non.treated)[names(Abundance.F25.Sample.non.treated) == "Abundance.F25.Sample.non.treated"] <- "RawAbundance"
Abundance.F26.Sample.treated <- data_raw %>%
select(Abundance.F26.Sample.treated)
Abundance.F26.Sample.treated["sample"] <- "Abundance.F26.Sample.treated"
names(Abundance.F26.Sample.treated)[names(Abundance.F26.Sample.treated) == "Abundance.F26.Sample.treated"] <- "RawAbundance"
Abundance.F27.Sample.treated <- data_raw %>%
select(Abundance.F27.Sample.treated)
Abundance.F27.Sample.treated["sample"] <- "Abundance.F27.Sample.treated"
names(Abundance.F27.Sample.treated)[names(Abundance.F27.Sample.treated) == "Abundance.F27.Sample.treated"] <- "RawAbundance"
Abundance.F28.Sample.treated <- data_raw %>%
select(Abundance.F28.Sample.treated)
Abundance.F28.Sample.treated["sample"] <- "Abundance.F28.Sample.treated"
names(Abundance.F28.Sample.treated)[names(Abundance.F28.Sample.treated) == "Abundance.F28.Sample.treated"] <- "RawAbundance"
Abundance.F29.Sample.treated <- data_raw %>%
select(Abundance.F29.Sample.treated)
Abundance.F29.Sample.treated["sample"] <- "Abundance.F29.Sample.treated"
names(Abundance.F29.Sample.treated)[names(Abundance.F29.Sample.treated) == "Abundance.F29.Sample.treated"] <- "RawAbundance"
Abundance.F30.Sample.treated <- data_raw %>%
select(Abundance.F30.Sample.treated)
Abundance.F30.Sample.treated["sample"] <- "Abundance.F30.Sample.treated"
names(Abundance.F30.Sample.treated)[names(Abundance.F30.Sample.treated) == "Abundance.F30.Sample.treated"] <- "RawAbundance"
##Add datasets vertically
MergedSamplesHepG2organic<- rbind(Abundance.F21.Sample.non.treated, Abundance.F22.Sample.non.treated, Abundance.F23.Sample.non.treated, Abundance.F24.Sample.non.treated, Abundance.F25.Sample.non.treated, Abundance.F26.Sample.treated, Abundance.F27.Sample.treated, Abundance.F28.Sample.treated, Abundance.F29.Sample.treated, Abundance.F30.Sample.treated)
SamplesRaw <- read.csv("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/Report/Ranalysis/MergedSamplesHepG2organic.csv")
# Define the number of colors you want
nb.cols <- 10
mycolors <- colorRampPalette(brewer.pal(8, "Set2"))(nb.cols)
# Create a ggplot with 18 colors
# Use scale_fill_manual
ggplot(SamplesRaw, aes(x=sample, y=log10(RawAbundance), color=sample)) +
geom_boxplot(show.legend = FALSE) +
labs(x="group") + theme_classic() +
theme(axis.text.x = element_text(angle = 45, hjust=1), axis.title.x = element_blank()) +
ggtitle("Raw abundances HepG2") +
scale_x_discrete (limits = c("Abundance.F21.Sample.non.treated", "Abundance.F22.Sample.non.treated", "Abundance.F23.Sample.non.treated", "Abundance.F24.Sample.non.treated", "Abundance.F25.Sample.non.treated", "Abundance.F26.Sample.treated", "Abundance.F27.Sample.treated", "Abundance.F28.Sample.treated", "Abundance.F29.Sample.treated", "Abundance.F30.Sample.treated")) +
scale_color_manual(values=c("Abundance.F21.Sample.non.treated" = "#FF9999", "Abundance.F22.Sample.non.treated"= "#FF9999", "Abundance.F23.Sample.non.treated"= "#FF9999", "Abundance.F24.Sample.non.treated"= "#FF9999", "Abundance.F25.Sample.non.treated"= "#FF9999", "Abundance.F26.Sample.treated"= "#99CCFF", "Abundance.F27.Sample.treated"= "#99CCFF", "Abundance.F28.Sample.treated"= "#99CCFF", "Abundance.F29.Sample.treated"= "#99CCFF", "Abundance.F30.Sample.treated"= "#99CCFF"))
# read the proteins.csv file
data_start <- read.csv("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/PD exports/Interfaphase/1854746152_AF_Huh7_organicPhase_NORM_percolator_Proteins.csv")
# master proteins only
newdata <- subset(data_start, Master=='IsMasterProtein')
# Protein FDR confidence = high
newdata2 <- subset(newdata, Protein.FDR.Confidence.Combined=='High')
# Select raw abundances
newdata3 <- newdata2[c(7, 64:72)]
# filter out proteins missing values
data_no_na <- na.omit(newdata3)
# remove sp Accessions (contaminants)
data_no_aa_noSP <- subset(data_no_na, Accession!='sp')
# fix the column headers
col_headers <- colnames(data_no_aa_noSP)
colnames(data_no_aa_noSP) <- col_headers
# save the annotations (gene symbol and protein accession) and remove from data frame
data_raw <- as.data.frame(data_no_aa_noSP[2:10])
Abundance.F31.Sample.non.treated <- data_raw %>%
select(Abundance.F31.Sample.non.treated)
Abundance.F31.Sample.non.treated["sample"] <- "Abundance.F31.Sample.non.treated"
names(Abundance.F31.Sample.non.treated)[names(Abundance.F31.Sample.non.treated) == "Abundance.F31.Sample.non.treated"] <- "RawAbundance"
Abundance.F32.Sample.non.treated <- data_raw %>%
select(Abundance.F32.Sample.non.treated)
Abundance.F32.Sample.non.treated["sample"] <- "Abundance.F32.Sample.non.treated"
names(Abundance.F32.Sample.non.treated)[names(Abundance.F32.Sample.non.treated) == "Abundance.F32.Sample.non.treated"] <- "RawAbundance"
Abundance.F33.Sample.non.treated <- data_raw %>%
select(Abundance.F33.Sample.non.treated)
Abundance.F33.Sample.non.treated["sample"] <- "Abundance.F33.Sample.non.treated"
names(Abundance.F33.Sample.non.treated)[names(Abundance.F33.Sample.non.treated) == "Abundance.F33.Sample.non.treated"] <- "RawAbundance"
Abundance.F34.Sample.non.treated <- data_raw %>%
select(Abundance.F34.Sample.non.treated)
Abundance.F34.Sample.non.treated["sample"] <- "Abundance.F34.Sample.non.treated"
names(Abundance.F34.Sample.non.treated)[names(Abundance.F34.Sample.non.treated) == "Abundance.F34.Sample.non.treated"] <- "RawAbundance"
Abundance.F35.Sample.treated <- data_raw %>%
select(Abundance.F35.Sample.treated)
Abundance.F35.Sample.treated["sample"] <- "Abundance.F35.Sample.treated"
names(Abundance.F35.Sample.treated)[names(Abundance.F35.Sample.treated) == "Abundance.F35.Sample.treated"] <- "RawAbundance"
Abundance.F36.Sample.treated <- data_raw %>%
select(Abundance.F36.Sample.treated)
Abundance.F36.Sample.treated["sample"] <- "Abundance.F36.Sample.treated"
names(Abundance.F36.Sample.treated)[names(Abundance.F36.Sample.treated) == "Abundance.F36.Sample.treated"] <- "RawAbundance"
Abundance.F37.Sample.treated <- data_raw %>%
select(Abundance.F37.Sample.treated)
Abundance.F37.Sample.treated["sample"] <- "Abundance.F37.Sample.treated"
names(Abundance.F37.Sample.treated)[names(Abundance.F37.Sample.treated) == "Abundance.F37.Sample.treated"] <- "RawAbundance"
Abundance.F38.Sample.treated <- data_raw %>%
select(Abundance.F38.Sample.treated)
Abundance.F38.Sample.treated["sample"] <- "Abundance.F38.Sample.treated"
names(Abundance.F38.Sample.treated)[names(Abundance.F38.Sample.treated) == "Abundance.F38.Sample.treated"] <- "RawAbundance"
Abundance.F39.Sample.treated <- data_raw %>%
select(Abundance.F39.Sample.treated)
Abundance.F39.Sample.treated["sample"] <- "Abundance.F39.Sample.treated"
names(Abundance.F39.Sample.treated)[names(Abundance.F39.Sample.treated) == "Abundance.F39.Sample.treated"] <- "RawAbundance"
##Add datasets vertically
MergedSamplesHuh7organic<- rbind(Abundance.F31.Sample.non.treated, Abundance.F32.Sample.non.treated, Abundance.F33.Sample.non.treated, Abundance.F34.Sample.non.treated, Abundance.F35.Sample.treated, Abundance.F36.Sample.treated, Abundance.F37.Sample.treated, Abundance.F38.Sample.treated, Abundance.F39.Sample.treated)
SamplesRaw <- read.csv("/Users/catarinafranco/Dropbox (Cambridge University)/Analysis/Willis/AlexFulton_OOPS_1854746152/Report/Ranalysis/MergedSamplesHuh7organic.csv")
ggplot(SamplesRaw, aes(x=sample, y=log10(RawAbundance), color=sample)) +
geom_boxplot(show.legend = FALSE) +
labs(x="group") + theme_classic() +
theme(axis.text.x = element_text(angle = 45, hjust=3), axis.title.x = element_blank()) +
ggtitle("Raw abundances Huh7 organic phase") +
scale_x_discrete (limits = c("Abundance.F31.Sample.non.treated", "Abundance.F32.Sample.non.treated", "Abundance.F33.Sample.non.treated", "Abundance.F34.Sample.non.treated", "Abundance.F35.Sample.treated", "Abundance.F36.Sample.treated", "Abundance.F37.Sample.treated", "Abundance.F38.Sample.treated", "Abundance.F39.Sample.treated")) +
scale_color_manual(values=c("Abundance.F31.Sample.non.treated" = "#FF9999", "Abundance.F32.Sample.non.treated"= "#FF9999", "Abundance.F33.Sample.non.treated"= "#FF9999", "Abundance.F34.Sample.non.treated"= "#FF9999", "Abundance.F35.Sample.treated"= "#99CCFF", "Abundance.F36.Sample.treated"= "#99CCFF", "Abundance.F37.Sample.treated"= "#99CCFF", "Abundance.F38.Sample.treated"= "#99CCFF", "Abundance.F39.Sample.treated"= "#99CCFF"))
Soon
For differential analysis you can download PD .txt files through the following links:
The description of the several columns on PD files can be found in the end of the report to help you navigate the data. Please do not hesitate in contacting us if there are any doubts.
When to include the Mass4Tox facility in the Acknowledgements Every time we have analysed your samples, helped you design the experiments or contributed in any way to your experiments, you must acknowledge our involvement/support in your publications (this includes all different type of publications e.g., papers, posters, reports, oral communications). Your acknowledgements will be used as metrics for the facility output and are of adamant importance to justify future funds requests (e.g., for new or upgrade(s) of the our mass spectrometers).
We also will ask you once you have a publication with results generated by the Mass4Tox Facility could you please inform Dr Catarina Franco (@cd735) so we can keep track of the acknowledgments.
How to Acknowledge Mass spectrometry analysis was/were performed at the Proteomics Facility of the Medical Research Council Toxicology Unit University of Cambridge, Cambridge, Uk. The authors would like to thank Dr Catarina Franco and Dr Bini Ramachandran for the help with manuscript preparation/sample preparation/data analysis/method development and/or providing access to (equipment e.g., AKTA pure) and/or helpful discussions on method development/sample analysis/experimental design.
When to consider authorship Whenever a member of the facility has made a significant intellectual contribution beyond routine analysis you should consider including the given member as a co-author of your publication. Please be mindful that even most of routine analysis have a lot of method development/benchmarking that happens behind the curtains so that we guarantee that the instrument is running in its best performance.